KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models

• URL: http://arxiv.org/abs/2506.19466v2
• Date: Fri, 27 Jun 2025 08:11:14 GMT
• Title: KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models
• Authors: Cheng Li, Jiexiong Liu, Yixuan Chen, Qihang Zhou, KunLun Meta,
• Abstract summary: KunLunBaizeRAG is a reinforcement learning-driven reasoning framework designed to enhance the reasoning capabilities of large language models (LLMs) in complex multi-hop question-answering tasks.<n>Key innovations include the RAG-driven Reasoning Alignment (RDRA) mechanism, the Search-Think Iterative Enhancement (STIE) mechanism, the Network-Local Intelligent Routing (NLR) mechanism, and a progressive hybrid training strategy.
• Score: 4.637288682081713
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces KunLunBaizeRAG, a reinforcement learning-driven reasoning framework designed to enhance the reasoning capabilities of large language models (LLMs) in complex multi-hop question-answering tasks. The framework addresses key limitations of traditional RAG, such as retrieval drift, information redundancy, and strategy rigidity. Key innovations include the RAG-driven Reasoning Alignment (RDRA) mechanism, the Search-Think Iterative Enhancement (STIE) mechanism, the Network-Local Intelligent Routing (NLR) mechanism, and a progressive hybrid training strategy. Experimental results demonstrate significant improvements in exact match (EM) and LLM-judged score (LJ) across four benchmarks, highlighting the framework's robustness and effectiveness in complex reasoning scenarios.

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